Chart libraries are mature. AI can scaffold a credible-looking analytics dashboard in an afternoon. So the build-vs-buy question feels settled, until you ship.
The cost of building embedded analytics didn’t disappear; it moved. It moved to the parts no demo shows: isolating tenant data so one customer can never see another’s, grounding an AI assistant so it doesn’t hallucinate or leak across accounts, clearing a security review bar that’s risen sharply since 2022, and maintaining all of it, forever, instead of shipping your actual product.
In 60 minutes, we’ll walk through what production-ready embedded analytics really takes in 2026, with real timelines and team costs — and a framework you can take straight to your own leadership.
What you’ll learn
- Why AI changed the starting line but not the finish line — and where homegrown analytics actually break
- The four hidden cost centers of building: multi-tenant data isolation, AI agent infrastructure, security & compliance, and never-ending maintenance
- What “production-ready” requires in 2026 — and why the security answers that passed in 2022 fail review now
- A clear, honest framework for when to build vs. when to buy (building is sometimes the right call — we’ll tell you when)
- How to make the decision with your own team, with numbers you can defend
Who should attend
CTOs, VPs of Engineering, Heads of Product, and senior engineers at SaaS companies and ISVs weighing whether to build or buy customer-facing analytics — especially anyone who’s started prototyping with AI and feels the pull to keep going in-house.
